Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

Deep Learning for Audio Signal Processing

Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross-fertilization between areas. The dominant feature representations (in particular, log-mel spectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e. audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified.Comment: 15 pages, 2 pdf figure

Distributed deep learning inference in fog networks

Today's smart devices are equipped with powerful integrated chips and built-in heterogeneous sensors that can leverage their potential to execute heavy computation and produce a large amount of sensor data. For instance, modern smart cameras integrate artificial intelligence to capture images that detect any objects in the scene and change parameters, such as contrast and color based on environmental conditions. The accuracy of the object recognition and classification achieved by intelligent applications has improved due to recent advancements in artificial intelligence (AI) and machine learning (ML), particularly, deep neural networks (DNNs). Despite the capability to carry out some AI/ML computation, smart devices have limited battery power and computing resources. Therefore, DNN computation is generally offloaded to powerful computing nodes such as cloud servers. However, it is challenging to satisfy latency, reliability, and bandwidth constraints in cloud-based AI. Thus, in recent years, AI services and tasks have been pushed closer to the end-users by taking advantage of the fog computing paradigm to meet these requirements. Generally, the trained DNN models are offloaded to the fog devices for DNN inference. This is accomplished by partitioning the DNN and distributing the computation in fog networks. This thesis addresses offloading DNN inference by dividing and distributing a pre-trained network onto heterogeneous embedded devices. Specifically, it implements the adaptive partitioning and offloading algorithm based on matching theory proposed in an article, titled "Distributed inference acceleration with adaptive dnn partitioning and offloading". The implementation was evaluated in a fog testbed, including Nvidia Jetson nano devices. The obtained results show that the adaptive solution outperforms other schemes (Random and Greedy) with respect to computation time and communication latency